


'''simple docstring'''


import argparse
import json
import pickle
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_						:  Optional[Any]				  =						logging.get_logger(__name__)







def      snake_case_  (    SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			int							= SwinConfig.from_pretrained(
				    """microsoft/swin-tiny-patch4-window7-224"""				, out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]	)
				_SCREAMING_SNAKE_CASE					:			Optional[Any]							= MaskFormerConfig(backbone_config=SCREAMING_SNAKE_CASE__	)

				_SCREAMING_SNAKE_CASE					:			Optional[int]							= """huggingface/label-files"""
				if "ade20k-full" in model_name:
								# this should be ok
								_SCREAMING_SNAKE_CASE					:			int							= 847
								_SCREAMING_SNAKE_CASE					:			Optional[int]							= """maskformer-ade20k-full-id2label.json"""
				elif "ade" in model_name:
								# this should be ok
								_SCREAMING_SNAKE_CASE					:			int							= 150
								_SCREAMING_SNAKE_CASE					:			Tuple							= """ade20k-id2label.json"""
				elif "coco-stuff" in model_name:
								# this should be ok
								_SCREAMING_SNAKE_CASE					:			Tuple							= 171
								_SCREAMING_SNAKE_CASE					:			str							= """maskformer-coco-stuff-id2label.json"""
				elif "coco" in model_name:
								# TODO
								_SCREAMING_SNAKE_CASE					:			str							= 133
								_SCREAMING_SNAKE_CASE					:			int							= """coco-panoptic-id2label.json"""
				elif "cityscapes" in model_name:
								# this should be ok
								_SCREAMING_SNAKE_CASE					:			Tuple							= 19
								_SCREAMING_SNAKE_CASE					:			Any							= """cityscapes-id2label.json"""
				elif "vistas" in model_name:
								# this should be ok
								_SCREAMING_SNAKE_CASE					:			Tuple							= 65
								_SCREAMING_SNAKE_CASE					:			List[str]							= """mapillary-vistas-id2label.json"""

				_SCREAMING_SNAKE_CASE					:			int							= json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, repo_type="""dataset"""	)				, """r"""	)	)
				_SCREAMING_SNAKE_CASE					:			Any							= {int(SCREAMING_SNAKE_CASE__	): v for k, v in idalabel.items()}

				return config







def      snake_case_  (    SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			Any							= []
				# stem
				# fmt: off
				rename_keys.append(("""backbone.patch_embed.proj.weight""", """model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight""")	)
				rename_keys.append(("""backbone.patch_embed.proj.bias""", """model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias""")	)
				rename_keys.append(("""backbone.patch_embed.norm.weight""", """model.pixel_level_module.encoder.model.embeddings.norm.weight""")	)
				rename_keys.append(("""backbone.patch_embed.norm.bias""", """model.pixel_level_module.encoder.model.embeddings.norm.bias""")	)
				# stages
				for i in range(len(config.backbone_config.depths	)	):
								for j in range(config.backbone_config.depths[i]	):
												rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm1.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")	)
												rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm1.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")	)
												rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")	)
												rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.relative_position_index""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")	)
												rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.proj.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")	)
												rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.proj.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")	)
												rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm2.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")	)
												rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm2.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")	)
												rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc1.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")	)
												rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc1.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")	)
												rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc2.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight""")	)
												rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc2.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias""")	)

								if i < 3:
												rename_keys.append((f"""backbone.layers.{i}.downsample.reduction.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight""")	)
												rename_keys.append((f"""backbone.layers.{i}.downsample.norm.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight""")	)
												rename_keys.append((f"""backbone.layers.{i}.downsample.norm.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias""")	)
								rename_keys.append((f"""backbone.norm{i}.weight""", f"""model.pixel_level_module.encoder.hidden_states_norms.{i}.weight""")	)
								rename_keys.append((f"""backbone.norm{i}.bias""", f"""model.pixel_level_module.encoder.hidden_states_norms.{i}.bias""")	)

				# FPN
				rename_keys.append(("""sem_seg_head.layer_4.weight""", """model.pixel_level_module.decoder.fpn.stem.0.weight""")	)
				rename_keys.append(("""sem_seg_head.layer_4.norm.weight""", """model.pixel_level_module.decoder.fpn.stem.1.weight""")	)
				rename_keys.append(("""sem_seg_head.layer_4.norm.bias""", """model.pixel_level_module.decoder.fpn.stem.1.bias""")	)
				for source_index, target_index in zip(range(3				, 0				, -1	)				, range(0				, 3	)	):
								rename_keys.append((f"""sem_seg_head.adapter_{source_index}.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight""")	)
								rename_keys.append((f"""sem_seg_head.adapter_{source_index}.norm.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight""")	)
								rename_keys.append((f"""sem_seg_head.adapter_{source_index}.norm.bias""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias""")	)
								rename_keys.append((f"""sem_seg_head.layer_{source_index}.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight""")	)
								rename_keys.append((f"""sem_seg_head.layer_{source_index}.norm.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight""")	)
								rename_keys.append((f"""sem_seg_head.layer_{source_index}.norm.bias""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias""")	)
				rename_keys.append(("""sem_seg_head.mask_features.weight""", """model.pixel_level_module.decoder.mask_projection.weight""")	)
				rename_keys.append(("""sem_seg_head.mask_features.bias""", """model.pixel_level_module.decoder.mask_projection.bias""")	)

				# Transformer decoder
				for idx in range(config.decoder_config.decoder_layers	):
								# self-attention out projection
								rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight""", f"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight""")	)
								rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias""", f"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias""")	)
								# cross-attention out projection
								rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight""")	)
								rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias""")	)
								# MLP 1
								rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight""", f"""model.transformer_module.decoder.layers.{idx}.fc1.weight""")	)
								rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias""", f"""model.transformer_module.decoder.layers.{idx}.fc1.bias""")	)
								# MLP 2
								rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight""", f"""model.transformer_module.decoder.layers.{idx}.fc2.weight""")	)
								rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias""", f"""model.transformer_module.decoder.layers.{idx}.fc2.bias""")	)
								# layernorm 1 (self-attention layernorm)
								rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight""", f"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight""")	)
								rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias""", f"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias""")	)
								# layernorm 2 (cross-attention layernorm)
								rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight""")	)
								rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias""")	)
								# layernorm 3 (final layernorm)
								rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight""", f"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight""")	)
								rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias""", f"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias""")	)

				rename_keys.append(("""sem_seg_head.predictor.transformer.decoder.norm.weight""", """model.transformer_module.decoder.layernorm.weight""")	)
				rename_keys.append(("""sem_seg_head.predictor.transformer.decoder.norm.bias""", """model.transformer_module.decoder.layernorm.bias""")	)

				# heads on top
				rename_keys.append(("""sem_seg_head.predictor.query_embed.weight""", """model.transformer_module.queries_embedder.weight""")	)

				rename_keys.append(("""sem_seg_head.predictor.input_proj.weight""", """model.transformer_module.input_projection.weight""")	)
				rename_keys.append(("""sem_seg_head.predictor.input_proj.bias""", """model.transformer_module.input_projection.bias""")	)

				rename_keys.append(("""sem_seg_head.predictor.class_embed.weight""", """class_predictor.weight""")	)
				rename_keys.append(("""sem_seg_head.predictor.class_embed.bias""", """class_predictor.bias""")	)

				for i in range(3	):
								rename_keys.append((f"""sem_seg_head.predictor.mask_embed.layers.{i}.weight""", f"""mask_embedder.{i}.0.weight""")	)
								rename_keys.append((f"""sem_seg_head.predictor.mask_embed.layers.{i}.bias""", f"""mask_embedder.{i}.0.bias""")	)
				# fmt: on

				return rename_keys







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			Tuple							= dct.pop(SCREAMING_SNAKE_CASE__	)
				_SCREAMING_SNAKE_CASE					:			Dict							= val







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			Tuple							= [int(backbone_config.embed_dim * 2**i	) for i in range(len(backbone_config.depths	)	)]
				for i in range(len(backbone_config.depths	)	):
								_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= num_features[i]
								for j in range(backbone_config.depths[i]	):
												# fmt: off
												# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
												_SCREAMING_SNAKE_CASE					:			int							= state_dict.pop(f"""backbone.layers.{i}.blocks.{j}.attn.qkv.weight"""	)
												_SCREAMING_SNAKE_CASE					:			Dict							= state_dict.pop(f"""backbone.layers.{i}.blocks.{j}.attn.qkv.bias"""	)
												# next, add query, keys and values (in that order) to the state dict
												_SCREAMING_SNAKE_CASE					:			str							= in_proj_weight[:dim, :]
												_SCREAMING_SNAKE_CASE					:			Any							= in_proj_bias[: dim]
												_SCREAMING_SNAKE_CASE					:			Tuple							= in_proj_weight[
												    dim : dim * 2, :
												]
												_SCREAMING_SNAKE_CASE					:			int							= in_proj_bias[
												    dim : dim * 2
												]
												_SCREAMING_SNAKE_CASE					:			List[str]							= in_proj_weight[
												    -dim :, :
												]
												_SCREAMING_SNAKE_CASE					:			Any							= in_proj_bias[-dim :]
												# fmt: on







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			Optional[int]							= config.decoder_config.hidden_size
				for idx in range(config.decoder_config.decoder_layers	):
								# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
								_SCREAMING_SNAKE_CASE					:			Tuple							= state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"""	)
								_SCREAMING_SNAKE_CASE					:			Tuple							= state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias"""	)
								# next, add query, keys and values (in that order) to the state dict
								_SCREAMING_SNAKE_CASE					:			str							= in_proj_weight[: hidden_size, :]
								_SCREAMING_SNAKE_CASE					:			Dict							= in_proj_bias[:config.hidden_size]
								_SCREAMING_SNAKE_CASE					:			List[Any]							= in_proj_weight[hidden_size : hidden_size * 2, :]
								_SCREAMING_SNAKE_CASE					:			List[Any]							= in_proj_bias[hidden_size : hidden_size * 2]
								_SCREAMING_SNAKE_CASE					:			Any							= in_proj_weight[-hidden_size :, :]
								_SCREAMING_SNAKE_CASE					:			Optional[int]							= in_proj_bias[-hidden_size :]
								# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
								_SCREAMING_SNAKE_CASE					:			Tuple							= state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"""	)
								_SCREAMING_SNAKE_CASE					:			Optional[int]							= state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias"""	)
								# next, add query, keys and values (in that order) to the state dict
								_SCREAMING_SNAKE_CASE					:			Tuple							= in_proj_weight[: hidden_size, :]
								_SCREAMING_SNAKE_CASE					:			Optional[int]							= in_proj_bias[:config.hidden_size]
								_SCREAMING_SNAKE_CASE					:			Optional[int]							= in_proj_weight[hidden_size : hidden_size * 2, :]
								_SCREAMING_SNAKE_CASE					:			Any							= in_proj_bias[hidden_size : hidden_size * 2]
								_SCREAMING_SNAKE_CASE					:			List[str]							= in_proj_weight[-hidden_size :, :]
								_SCREAMING_SNAKE_CASE					:			str							= in_proj_bias[-hidden_size :]
				# fmt: on







def      snake_case_  (    ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			int							= """http://images.cocodataset.org/val2017/000000039769.jpg"""
				_SCREAMING_SNAKE_CASE					:			str							= Image.open(requests.get(SCREAMING_SNAKE_CASE__				, stream=SCREAMING_SNAKE_CASE__	).raw	)
				return im







@torch.no_grad()
def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__ = False	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			int							= get_maskformer_config(SCREAMING_SNAKE_CASE__	)

				# load original state_dict
				with open(SCREAMING_SNAKE_CASE__				, """rb"""	) as f:
								_SCREAMING_SNAKE_CASE					:			Dict							= pickle.load(SCREAMING_SNAKE_CASE__	)
				_SCREAMING_SNAKE_CASE					:			List[Any]							= data["""model"""]

				# for name, param in state_dict.items():
				#     print(name, param.shape)

				# rename keys
				_SCREAMING_SNAKE_CASE					:			Tuple							= create_rename_keys(SCREAMING_SNAKE_CASE__	)
				for src, dest in rename_keys:
								rename_key(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)
				read_in_swin_q_k_v(SCREAMING_SNAKE_CASE__				, config.backbone_config	)
				read_in_decoder_q_k_v(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)

				# update to torch tensors
				for key, value in state_dict.items():
								_SCREAMING_SNAKE_CASE					:			Optional[Any]							= torch.from_numpy(SCREAMING_SNAKE_CASE__	)

				# load 🤗 model
				_SCREAMING_SNAKE_CASE					:			str							= MaskFormerForInstanceSegmentation(SCREAMING_SNAKE_CASE__	)
				model.eval()

				for name, param in model.named_parameters():
								print(SCREAMING_SNAKE_CASE__				, param.shape	)

				_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= model.load_state_dict(SCREAMING_SNAKE_CASE__				, strict=SCREAMING_SNAKE_CASE__	)
				assert missing_keys == [
				    "model.pixel_level_module.encoder.model.layernorm.weight",
				    "model.pixel_level_module.encoder.model.layernorm.bias",
				]
				assert len(SCREAMING_SNAKE_CASE__	) == 0, f"""Unexpected keys: {unexpected_keys}"""

				# verify results
				_SCREAMING_SNAKE_CASE					:			str							= prepare_img()
				if "vistas" in model_name:
								_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= 65
				elif "cityscapes" in model_name:
								_SCREAMING_SNAKE_CASE					:			Optional[Any]							= 6_5535
				else:
								_SCREAMING_SNAKE_CASE					:			List[Any]							= 255
				_SCREAMING_SNAKE_CASE					:			List[str]							= True if """ade""" in model_name else False
				_SCREAMING_SNAKE_CASE					:			Any							= MaskFormerImageProcessor(ignore_index=SCREAMING_SNAKE_CASE__				, reduce_labels=SCREAMING_SNAKE_CASE__	)

				_SCREAMING_SNAKE_CASE					:			str							= image_processor(SCREAMING_SNAKE_CASE__				, return_tensors="""pt"""	)

				_SCREAMING_SNAKE_CASE					:			Optional[Any]							= model(**SCREAMING_SNAKE_CASE__	)

				print("""Logits:"""				, outputs.class_queries_logits[0, :3, :3]	)

				if model_name == "maskformer-swin-tiny-ade":
								_SCREAMING_SNAKE_CASE					:			str							= torch.tensor(
								    [[3.6_3_5_3, -4.4_7_7_0, -2.6_0_6_5], [0.5_0_8_1, -4.2_3_9_4, -3.5_3_4_3], [2.1_9_0_9, -5.0_3_5_3, -1.9_3_2_3]]	)
				assert torch.allclose(outputs.class_queries_logits[0, :3, :3]				, SCREAMING_SNAKE_CASE__				, atol=1E-4	)
				print("""Looks ok!"""	)

				if pytorch_dump_folder_path is not None:
								print(f"""Saving model and image processor to {pytorch_dump_folder_path}"""	)
								Path(SCREAMING_SNAKE_CASE__	).mkdir(exist_ok=SCREAMING_SNAKE_CASE__	)
								model.save_pretrained(SCREAMING_SNAKE_CASE__	)
								image_processor.save_pretrained(SCREAMING_SNAKE_CASE__	)

				if push_to_hub:
								print("""Pushing model and image processor to the hub..."""	)
								model.push_to_hub(f"""nielsr/{model_name}"""	)
								image_processor.push_to_hub(f"""nielsr/{model_name}"""	)


if __name__ == "__main__":
				UpperCAmelCase_						:  Union[str, Any]				  =						argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--model_name',
				    default='maskformer-swin-tiny-ade',
				    type=str,
				    help=('Name of the MaskFormer model you\'d like to convert',),
				)
				parser.add_argument(
				    '--checkpoint_path',
				    default='/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl',
				    type=str,
				    help='Path to the original state dict (.pth file).',
				)
				parser.add_argument(
				    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
				)
				parser.add_argument(
				    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
				)

				UpperCAmelCase_						:  List[Any]				  =						parser.parse_args()
				convert_maskformer_checkpoint(
				    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
				)








'''simple docstring'''


import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
				from .tokenization_mbart import MBartTokenizer
else:
				UpperCAmelCase_						:  List[str]				  =						None


UpperCAmelCase_						:  Tuple				  =						logging.get_logger(__name__)


UpperCAmelCase_						:  List[Any]				  =						{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

UpperCAmelCase_						:  Dict				  =						{
    'vocab_file': {
        'facebook/mbart-large-en-ro': (
            'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'
        ),
        'facebook/mbart-large-cc25': (
            'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/mbart-large-en-ro': 'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json',
        'facebook/mbart-large-cc25': 'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json',
    },
}

UpperCAmelCase_						:  str				  =						{
    'facebook/mbart-large-en-ro': 1024,
    'facebook/mbart-large-cc25': 1024,
}

# fmt: off
UpperCAmelCase_						:  List[str]				  =						['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN']





class        lowercase__    (		_snake_case   ):





							'''simple docstring'''


							A_				: Any       =    VOCAB_FILES_NAMES
							A_				: List[str]       =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							A_				: Any       =    PRETRAINED_VOCAB_FILES_MAP
							A_				: Tuple       =    ["""input_ids""", """attention_mask"""]
							A_				: Dict       =    MBartTokenizer

							A_				: List[int]       =    []
							A_				: List[int]       =    []






							def __init__(       self       ,   __snake_case=None       ,   __snake_case=None       ,   __snake_case="<s>"       ,   __snake_case="</s>"       ,   __snake_case="</s>"       ,   __snake_case="<s>"       ,   __snake_case="<unk>"       ,   __snake_case="<pad>"       ,   __snake_case="<mask>"       ,   __snake_case=None       ,   __snake_case=None       ,   __snake_case=None       ,   **__snake_case       ,   ):
											# Mask token behave like a normal word, i.e. include the space before it
											_SCREAMING_SNAKE_CASE					:			List[Any]							= AddedToken(__snake_case       ,   lstrip=__snake_case       ,   rstrip=__snake_case    ) if isinstance(__snake_case       ,   __snake_case    ) else mask_token

											super().__init__(
											    vocab_file=__snake_case       ,   tokenizer_file=__snake_case       ,   bos_token=__snake_case       ,   eos_token=__snake_case       ,   sep_token=__snake_case       ,   cls_token=__snake_case       ,   unk_token=__snake_case       ,   pad_token=__snake_case       ,   mask_token=__snake_case       ,   src_lang=__snake_case       ,   tgt_lang=__snake_case       ,   additional_special_tokens=__snake_case       ,   **__snake_case       ,   )

											_SCREAMING_SNAKE_CASE					:			List[str]							= vocab_file
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= False if not self.vocab_file else True

											_SCREAMING_SNAKE_CASE					:			int							= FAIRSEQ_LANGUAGE_CODES.copy()

											if additional_special_tokens is not None:
															# Only add those special tokens if they are not already there.
															_additional_special_tokens.extend(
															    [t for t in additional_special_tokens if t not in _additional_special_tokens]    )

											self.add_special_tokens({"""additional_special_tokens""": _additional_special_tokens}    )
											_SCREAMING_SNAKE_CASE					:			str							= {
											    lang_code: self.convert_tokens_to_ids(__snake_case    ) for lang_code in FAIRSEQ_LANGUAGE_CODES
											}

											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= src_lang if src_lang is not None else """en_XX"""
											_SCREAMING_SNAKE_CASE					:			str							= self.convert_tokens_to_ids(self._src_lang    )
											_SCREAMING_SNAKE_CASE					:			Any							= tgt_lang
											self.set_src_lang_special_tokens(self._src_lang    )






							@property
							def UpperCAmelCase_      (       self    ):
											return self._src_lang






							@src_lang.setter
							def UpperCAmelCase_      (       self       ,   __snake_case    ):
											_SCREAMING_SNAKE_CASE					:			Any							= new_src_lang
											self.set_src_lang_special_tokens(self._src_lang    )






							def UpperCAmelCase_      (       self       ,   __snake_case       ,   __snake_case = None    ):
											if token_ids_a is None:
															return self.prefix_tokens + token_ids_a + self.suffix_tokens
											# We don't expect to process pairs, but leave the pair logic for API consistency
											return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens






							def UpperCAmelCase_      (       self       ,   __snake_case       ,   __snake_case = None    ):
											_SCREAMING_SNAKE_CASE					:			List[str]							= [self.sep_token_id]
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= [self.cls_token_id]

											if token_ids_a is None:
															return len(cls + token_ids_a + sep    ) * [0]
											return len(cls + token_ids_a + sep + sep + token_ids_a + sep    ) * [0]






							def UpperCAmelCase_      (       self       ,   __snake_case       ,   __snake_case       ,   __snake_case       ,   __snake_case       ,   **__snake_case    ):
											if src_lang is None or tgt_lang is None:
															raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""    )
											_SCREAMING_SNAKE_CASE					:			Dict							= src_lang
											_SCREAMING_SNAKE_CASE					:			List[Any]							= self(__snake_case       ,   add_special_tokens=__snake_case       ,   return_tensors=__snake_case       ,   **__snake_case    )
											_SCREAMING_SNAKE_CASE					:			Dict							= self.convert_tokens_to_ids(__snake_case    )
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= tgt_lang_id
											return inputs






							def UpperCAmelCase_      (       self       ,   __snake_case       ,   __snake_case = "en_XX"       ,   __snake_case = None       ,   __snake_case = "ro_RO"       ,   **__snake_case       ,   ):
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= src_lang
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= tgt_lang
											return super().prepare_seqaseq_batch(__snake_case       ,   __snake_case       ,   **__snake_case    )






							def UpperCAmelCase_      (       self    ):
											return self.set_src_lang_special_tokens(self.src_lang    )






							def UpperCAmelCase_      (       self    ):
											return self.set_tgt_lang_special_tokens(self.tgt_lang    )






							def UpperCAmelCase_      (       self       ,   __snake_case    ):
											_SCREAMING_SNAKE_CASE					:			List[str]							= self.convert_tokens_to_ids(__snake_case    )
											_SCREAMING_SNAKE_CASE					:			str							= []
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= [self.eos_token_id, self.cur_lang_code]

											_SCREAMING_SNAKE_CASE					:			int							= self.convert_ids_to_tokens(self.prefix_tokens    )
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= self.convert_ids_to_tokens(self.suffix_tokens    )

											_SCREAMING_SNAKE_CASE					:			Optional[int]							= processors.TemplateProcessing(
											    single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str       ,   pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str       ,   special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str       ,   self.prefix_tokens + self.suffix_tokens    )    )       ,   )






							def UpperCAmelCase_      (       self       ,   __snake_case    ):
											_SCREAMING_SNAKE_CASE					:			List[str]							= self.convert_tokens_to_ids(__snake_case    )
											_SCREAMING_SNAKE_CASE					:			List[str]							= []
											_SCREAMING_SNAKE_CASE					:			Tuple							= [self.eos_token_id, self.cur_lang_code]

											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= self.convert_ids_to_tokens(self.prefix_tokens    )
											_SCREAMING_SNAKE_CASE					:			str							= self.convert_ids_to_tokens(self.suffix_tokens    )

											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= processors.TemplateProcessing(
											    single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str       ,   pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str       ,   special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str       ,   self.prefix_tokens + self.suffix_tokens    )    )       ,   )






							def UpperCAmelCase_      (       self       ,   __snake_case       ,   __snake_case = None    ):
											if not self.can_save_slow_tokenizer:
															raise ValueError(
															    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
															    """tokenizer."""    )

											if not os.path.isdir(__snake_case    ):
															logger.error(f"""Vocabulary path ({save_directory}) should be a directory."""    )
															return
											_SCREAMING_SNAKE_CASE					:			List[str]							= os.path.join(
											    __snake_case       ,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]    )

											if os.path.abspath(self.vocab_file    ) != os.path.abspath(__snake_case    ):
															copyfile(self.vocab_file       ,   __snake_case    )

											return (out_vocab_file,)




